Databricks-ML-Assoc Model Development Practice Question
Which THREE factors should be considered when selecting a model for deployment in a production Databricks environment?
⚠ Common exam trap
Candidates often overlook non-technical factors like interpretability, focusing strictly on performance metrics like accuracy, which is insufficient for production requirements where business stakeholders need model transparency.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The average inference latency for a single prediction.
Deployment involves balancing performance, cost, and maintainability. Latency (performance), interpretability (business requirement), and resource requirements (cost/scalability) are the three pillars of a production-ready model. Failing to consider any of these can lead to models that work well in a notebook but fail to meet business needs or exceed operational budgets, causing significant issues during the transition from experimentation to production.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
The average inference latency for a single prediction.
Why this is correct
Latency is critical for user-facing applications. If a model takes too long to respond, it may be unusable for real-time applications. Understanding the latency requirements of the business use case and ensuring the model meets them is essential for successful deployment and positive user experience in a production scenario.
- ✗
The number of lines of code in the training notebook.
Why it's wrong here
Code length is a poor metric for evaluating model suitability for deployment. A concise script might produce a computationally expensive model, while a verbose notebook might result in a highly efficient one. Quality, maintainability, and model performance are far more important than the literal line count of the source notebook file.
- ✓
The interpretability requirements of the end users.
Why this is correct
In many industries, especially finance and healthcare, stakeholders need to understand why a model made a specific decision. Choosing a 'black box' model when interpretability is a requirement will lead to rejection of the model in production, regardless of its accuracy metrics. This must be assessed early in the development lifecycle.
- ✓
The hardware resource requirements for inference.
Why this is correct
Different models have vastly different compute footprints. A high-memory, GPU-intensive model will be significantly more expensive and complex to maintain in production than a lightweight, CPU-based model. Assessing the infrastructure needs helps determine the total cost of ownership and ensures the model is compatible with available production hosting resources.
- ✗
The number of times the model was saved to DBFS.
Why it's wrong here
How often a model was saved is irrelevant to its production readiness. This is a development artifact that does not correlate with performance, latency, cost, or interpretability. Focusing on this metric would be a distraction from the actual performance and operational criteria that determine whether a model is ready for production.
About these practice questions
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Databricks exam blueprint
This Databricks-ML-Assoc practice question is part of Courseiva's free Databricks certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Databricks-ML-Assoc exam.